Quality control of AIRS total column ozone data within tropical cyclones

Abstract

The Atmospheric Infrared Sounder (AIRS) provides infrared radiance observations twice daily, which can be used to retrieve total column ozone with high spatial resolution. However, it was found that almost all of the ozone data within typhoons and hurricanes were flagged to be of bad quality by the AIRS original quality control (QC) scheme. This determination was based on the ratio of total precipitable water (TPW) error divided by TPW value, where TPW was an AIRS retrieval product. It was found that the difficulty in finding total column ozone data that could pass AIRS QC was related to the low TPW employed in the AIRS QC algorithm. In this paper, a new two-step QC scheme for AIRS total column ozone is developed. A new ratio is defined which replaces the AIRS TPW with the zonal mean TPW retrieved from the Advanced Microwave Sounding Unit. The first QC step is to remove outliers when the new ratio exceeds 33%. Linear regression models between total column ozone and mean potential vorticity are subsequently developed with daily updates, which are required for future applications of the proposed total ozone QC algorithm to vortex initialization and assimilation of AIRS data. In the second QC step, observations that significantly deviate from the models are further removed using a biweighting algorithm. Numerical results for two typhoon cases and two hurricane cases show that a large amount of good quality AIRS total ozone data is kept within Tropical Cyclones after implementing the proposed QC algorithm.

Keywords

AIRS total column ozone / total precipitable water / mean potential vorticity / quality control

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